Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments (2401.05946v1)

Published 11 Jan 2024 in cs.LG and cs.AI

Abstract: Despite their stellar performance on a wide range of tasks, including in-context tasks only revealed during inference, vanilla transformers and variants trained for next-token predictions (a) do not learn an explicit world model of their environment which can be flexibly queried and (b) cannot be used for planning or navigation. In this paper, we consider partially observed environments (POEs), where an agent receives perceptually aliased observations as it navigates, which makes path planning hard. We introduce a transformer with (multiple) discrete bottleneck(s), TDB, whose latent codes learn a compressed representation of the history of observations and actions. After training a TDB to predict the future observation(s) given the history, we extract interpretable cognitive maps of the environment from its active bottleneck(s) indices. These maps are then paired with an external solver to solve (constrained) path planning problems. First, we show that a TDB trained on POEs (a) retains the near perfect predictive performance of a vanilla transformer or an LSTM while (b) solving shortest path problems exponentially faster. Second, a TDB extracts interpretable representations from text datasets, while reaching higher in-context accuracy than vanilla sequence models. Finally, in new POEs, a TDB (a) reaches near-perfect in-context accuracy, (b) learns accurate in-context cognitive maps (c) solves in-context path planning problems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. Deepmind lab. arXiv preprint arXiv:1612.03801, 2016.
  2. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  3. Measuring disentanglement: A review of metrics. arXiv preprint arXiv:2012.09276, 2020.
  4. Decision transformer: Reinforcement learning via sequence modeling. Advances in neural information processing systems, 34:15084–15097, 2021a.
  5. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021b.
  6. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  7. Lonnie Chrisman. Reinforcement learning with perceptual aliasing: The perceptual distinctions approach. In AAAI, volume 1992, pages 183–188. Citeseer, 1992.
  8. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.
  9. Towards automated circuit discovery for mechanistic interpretability. arXiv preprint arXiv:2304.14997, 2023.
  10. Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860, 2019.
  11. Maximum likelihood from incomplete data via the em algorithm. Journal of the royal statistical society: series B (methodological), 39(1):1–22, 1977.
  12. A mathematical framework for transformer circuits. Transformer Circuits Thread, 1, 2021.
  13. Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps. Nature communications, 12(1):2392, 2021.
  14. Factorial hidden markov models. Advances in Neural Information Processing Systems, 8, 1995.
  15. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284, 2020.
  16. Leveraging pre-trained large language models to construct and utilize world models for model-based task planning. arXiv preprint arXiv:2305.14909, 2023.
  17. Graph schemas as abstractions for transfer learning, inference, and planning. arXiv preprint arXiv:2302.07350, 2023.
  18. Byol-explore: Exploration by bootstrapped prediction. Advances in neural information processing systems, 35:31855–31870, 2022.
  19. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008.
  20. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016.
  21. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
  22. An algorithm for drawing general undirected graphs. Information processing letters, 31(1):7–15, 1989.
  23. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  24. Guaranteed discovery of control-endogenous latent states with multi-step inverse models. Transactions on Machine Learning Research, 2022.
  25. Emergent world representations: Exploring a sequence model trained on a synthetic task. arXiv preprint arXiv:2210.13382, 2022.
  26. Does circuit analysis interpretability scale? evidence from multiple choice capabilities in chinchilla. arXiv preprint arXiv:2307.09458, 2023.
  27. Gaia: a benchmark for general ai assistants. arXiv preprint arXiv:2311.12983, 2023.
  28. Evaluating cognitive maps and planning in large language models with cogeval. arXiv preprint arXiv:2309.15129, 2023.
  29. Progress measures for grokking via mechanistic interpretability. arXiv preprint arXiv:2301.05217, 2023.
  30. In-context learning and induction heads. arXiv preprint arXiv:2209.11895, 2022.
  31. Plansformer: Generating symbolic plans using transformers. arXiv preprint arXiv:2212.08681, 2022.
  32. Improving language understanding by generative pre-training. 2018.
  33. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821–8831. PMLR, 2021.
  34. A generalist agent. arXiv preprint arXiv:2205.06175, 2022.
  35. A distance measure between attributed relational graphs for pattern recognition. IEEE transactions on systems, man, and cybernetics, (3):353–362, 1983.
  36. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022.
  37. Schema-learning and rebinding as mechanisms of in-context learning and emergence. arXiv preprint arXiv:2307.01201, 2023.
  38. Large language models still can’t plan (a benchmark for llms on planning and reasoning about change). arXiv preprint arXiv:2206.10498, 2022.
  39. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
  40. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  41. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682, 2022.
  42. An explanation of in-context learning as implicit bayesian inference. arXiv preprint arXiv:2111.02080, 2021.
Citations (2)
View on Semantic Scholar

Summary

  • The paper introduces the Transformer with Discrete Bottleneck (TDB) model that learns compact cognitive maps for efficient planning in partially observed environments.
  • The paper leverages multiple discrete bottlenecks to compress observations into latent codes, overcoming perceptual aliasing and expediting navigation tasks.
  • The paper demonstrates emergent in-context learning with accurate map inference while highlighting challenges like redundant feature representation and limited applicability to continuous data.

Overview

In the field of artificial intelligence, engineers and researchers strive to create models that not only understand and predict sequences but also navigate and plan within an environment. A predominant tool in this field has been the transformer, a type of neural network renowned for its performance in sequence modeling tasks. However, transformers are not without their limitations; crucially, they lack the capabilities for explicit planning and navigation.

A recently proposed model called the Transformer with Discrete Bottleneck (TDB) offers potential solutions to these limitations. The TDB is designed to create compact representations of an agent's history within an environment and use these representations to efficiently solve path planning problems.

The Transformer Challenge in Partially Observed Environments

Transformers are adept at sequence prediction, learning to mimic the next element in a series based on previous data. But in environments where only partial observations are available—such as a robot navigating a maze with limited sensors—traditional transformers falter. They fail to generate a detailed internal model of the world that they can later query for making decisions or navigating through space. Specifically, when faced with similar-looking but distinct locations or sequences of events (a problem known as perceptual aliasing), these models cannot reliably inform an agent of its precise location or path to a goal.

Introducing the Transformer with Discrete Bottleneck (TDB)

To tackle these specific challenges, the TDB introduces multiple discrete bottlenecks into the architecture. These bottlenecks compress output from the transformer into a finite set of latent codes that effectively summarize the agent's observations and actions up to a given point. These codes serve as inputs to an external solver, which can handle complex path planning problems.

Advantages of TDB

The key advantage of TDB over traditional transformers and long short-term memory networks (LSTMs) is its ability to retain excellent prediction capabilities while exponentially accelerating the path planning process. Through empirical evaluations in various simulated environments, from textured 3D spaces to textual datasets, TDB has demonstrated superior results in both predicting future observations and efficiently navigating to defined targets.

Furthermore, TDB was shown to be capable of inferring accurate cognitive maps of new environments without any prior exposure, a phenomenon described as "emergent in-context learning." These maps provide an interpretable guide for the agent's movements and decisions, exhibiting clear distinctions between different 'concepts' or areas within the environment.

Future Horizons and Challenges

Despite its promising results, TDB is not without its own challenges. One of the limitations is its reliance on categorical input data, making it currently unsuitable for more general applications involving continuous observations such as raw sensory data. Moreover, while the introduction of multiple discrete bottlenecks has been shown to expedite training, they tend to learn overlapping representations, leading to redundancy in the model's knowledge.

Moving forward, efforts will pivot towards enabling TDB to accept a broader range of inputs, and refining the model to encourage bottlenecks to capture distinct and non-redundant features of the environment. This evolution would significantly enhance TDB's utility in planning-compatible world models and complex decision-making scenarios.

Conclusion

The Transformer with Discrete Bottleneck (TDB) represents a pivotal development in the quest to imbue AI with the capacity for efficient planning and navigation in partially observed environments. Its unique structure captures the nuance of an environment's dynamics while maintaining the predictive prowess customary to transformers. As AI continues to push the boundaries of autonomous operation and in-context adaptability, models like TDB will inevitably become invaluable players in the unfolding narrative of machine intelligence.